Quantum Computing Applications in Financial Modeling and Portfolio Optimization

In this decade the classical computers can surpass the computational power anticipated using quantum computers, bringing transformative advancements across multiple industries, with finance expected to be the first to reap the benefits. Notably, the quantum computer poised in the financial sector leveraging medium and long terms usually but sometimes in short term. The present study involves the quantum computing applications in financial modelling and portfolio optimization using classical convex optimization algorithmthat focus on the current advancement of quantum computationsin financial modelling. The key areas focused by quantum computing includes machine learning optimization and stochastic modelling. It explodes the quantum computing techniques adopted with solutions that can enhance the problem-solving skills in finance sector like fraud detection, natural language processing, portfolio optimization, risk modelling, derivative pricing that improves both efficiency and accuracy. Additionally, we examine the practicality of implementing these algorithms on near-term quantum computers across different hardware architectures and highlight their relevance to a diverse range of financial use cases.